Identification of High and Low-Risk Areas of Tuberculosis in Lorestan Province, West of Iran.

Background
Nowadays, tuberculosis (TB)-an infectious disease caused by Mycobacterium tuberculosis-presents with different location patterns. Spatial analysis is one of the most important tools to detect and monitor public health disease patterns. This study aimed to identify the low and high-risk areas in Lorestan Province (west of Iran) to help the health programmer for the best intervention.


Materials and Methods
Lorestan has 9 counties, 22 cities, 25 zones, 81 villages, and 2842 residential villages. Our study cases were 1481 patients registered in the TB center of Lorestan Province. We investigated the spatial distribution of TB in Lorestan between 2002 and 2008 using a multilevel model. STATA Ver. 10 software was used for the data analysis.


Results
The multilevel model was a better fit to the data for the spatial correlation structure. It adjusted relative risks by borrowing information of the neighboring areas in each village. Maximum risk of disease was seen in the central zone of Koram-Abad, and all villages of Delphan were identified as low-risk areas.


Conclusion
Various factors such as improvement of socio-economic conditions, implementation of programs, culture, genetic background, health-related behavior, and lifestyle can influence TB control substantially. A deprived region located in the southern part of Khoram-Abad was identified as the highest risk area in our study. The poor socio-economic structure can be an important factor for the increased risk of TB in this region.


INTRODUCTION
Tuberculosis (TB) is the most infectious disease and caused by Mycobacterium tuberculosis (1). The World Health Organization declared it as a global public health emergency in 1993 (2). It is transmitted under various situations associated with lifestyles, such as shelters of homeless people, bars, and prisons (3)(4)(5). The transmission of infectious pathogens from infected to susceptible individuals diminishes by increasing the distance between them (6). Close contact between two individuals having a conversation within restricted spaces with inappropriate and insufficient ventilation is a prerequisite for being infected (3,7). TB causes about 1.7 million deaths in the world each year (8). There are about 9 million new TB cases annually.
Its prevalence was estimated at 12 million cases by the World Health Organization in 2011 (2,8). According to the statistical figures published by the Ministry of Health and Medical Education, the TB incidence rate is 13 per hundred thousand in Iran (9). The incidence and prevalence rate of TANAFFOS Yazdani- Charati J,et al. 271 Tanaffos 2017; 16 (4): [270][271][272][273][274][275][276] TB is higher in the border areas of Iran, such as Sistan and Balochistan, Khorasan, Mazandaran, Gilan, West Azerbaijan, East Azerbaijan, Ardabil, Kurdistan, Khuzestan provinces, and South Beach, and these rates were low in the central parts of Iran (10,11).
Tuberculosis, like many infectious diseases, is prone to spatial aggregation or clustering (6,12). Previous studies of Ardebil. For example, the risk of the disease was lower in the rural areas compared with the urban areas of Mazandaran, and the incidence rate in the Tonekabon and Behshahr cities was higher than the mean incidence rate of the province (9,17).
TB is associated with behavioral and demographic factors such as poor nutrition, tobacco and alcohol consumption, age, and household crowding (18,19).
Regardless of the improvements in TB control plans during the past decades, TB continues to increase globally(3). Spatial analysis of disease prevalence and incidence has been a branch of epidemiology, public health, and the investigation of disease in human populations (20). It can be very valuable for the cost-effective intervention planning (1). Some studies of the location effects on health are based on the multilevel approach that studies geographical variations of health events by fragmenting space into arbitrary parts (21).
This study aimed to identify the high-risk areas and study the demographic patterns of this disease using an advanced statistical method consisting of a village, zone, and county. To study the spatial pattern of TB in Lorestan, we conducted a multilevel analysis at three levels. This structure takes into account the spatial autocorrelation between all the levels and smoothes the estimated risks of disease by borrowing information from neighbor regions.
Lorestan has a mountainous and cold climate. The province is surrounded by Hamedan from north, Isfahan and Markazi Province from east, Kermanshah and Ilam from the west, and Khuzestan from the southern direction.
It has a small border with Chahar-Mahal and Bakhtiari as well (22).

MATERIALS AND METHODS
The presented observational ecological study is based Although we have a three-level structure that we can fit in our model, it sounds better to ignore this structure and fit a simple Poisson regression model early (18). We saw that the Poisson assumption was not a good fit to the data as there is a far greater variability than would be expected from a Poisson distribution. This suggests that, in fact, the villages are not independent Poisson counts and that we should take account of some of the structure in the data by fitting random effects for the zones and counties. This will result in a Poisson response multilevel model.

DISCUSSION
Although the incidence of TB is reducing in some  contribute to health programming. Data collection over several years will be able to recognize spatial and temporal changes in the pattern of TB (28).

CONCLUSION
This study describes the spatial distribution of TB across Lorestan and explores high and low-risk regions allowing for spatial autocorrelation at three levels. As efforts continue to bring TB under control in the potential areas, the spatial information can help to build efforts to target ongoing strategies, and risk factor epidemiology provides important information on who remains at risk of infection. TB in different regions continues to be ecologically associated with many of the traditional sociodemographic markers of low social and economic status. Efforts to reach high-risk low-status groups still have the potential to reap benefits.